Does Media Format Matter? Investigating the Toxicity, Sentiment and Topic of Audio Versus Text Social Media Messages
Bibliographic record
Abstract
Audio messaging and voice-based interactions are growing in popularity. Lexical features of a manually-curated dataset of real-world audio tweets, as well as text and video/image tweets from the same user accounts, are analyzed to explore how user-generated audio differs from text. The toxicity, sentiment, topic and length of audio tweet transcripts are compared with their accompanying text, date-matched text tweets from the same users and date-matched video/image tweets and their accompanying text. Audio tweets were significantly less toxic than both text tweets and text that accompanied the audio tweet, as well as significantly lower sentiment than their accompanying text. The topics and word counts of audio, text and video/image tweets also differed. These findings are then used to derive design implications for audio and conversational agent interaction. This research contributes preliminary insights about audio social media messages that may help researchers and designers of audio- and agent-based interaction better understand and design for different media formats.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".